Keynote Speakers | 大会专家报告

Keynote Speakers

Prof. Dan Zhang

York University, Canada
（加拿大约克大学）

Biography: Dr. Dan Zhang is a Kaneff Professor and Tier 1 York Research Chair in Advanced Robotics and Mechatronics at York University. Before 2016, Dr. Zhang was a Professor and Canada Research Chair in Advanced Robotics and Automation, and was a founding Chair of the Department of Automotive, Mechanical, and Manufacturing Engineering at the University of Ontario Institute of Technology. He received his Ph.D. in Mechanical Engineering from Laval University, Canada, in June 2000.
Dr. Zhang's research interests include robotics and mechatronics; high performance parallel robotic machine development; sustainable/green manufacturing systems; rehabilitation robot and rescue robot.
Dr. Zhang’s contributions to and leadership within the field of robotic and automation have been recognized with several prestigious awards, within his own university (Research Excellence Award both from university level and faculty level) and Kaneff Professorship, the Province of Ontario (Early Researcher Award), the professional societies (Fellow of the ASME, the CAE, the EIC and the CSME), and federal funding agencies (Canada Research Chair in January 2009 and renewed in January 2014).
Dr. Zhang is the editor-in-chief for International Journal of Mechanisms and Robotic Systems, the editor-in-chief for International Journal of Robotics Applications and Technologies. Dr. Zhang served as a member of Natural Sciences and Engineering Research Council of Canada (NSERC) Grant Selection Committee.
Dr. Zhang is a Fellow of the Canadian Academy of Engineering (CAE), a Fellow of the Engineering Institute of Canada (EIC), a Fellow of American Society of Mechanical Engineers (ASME), and a Fellow of Canadian Society for Mechanical Engineering (CSME), a Senior Member IEEE, and a Senior Member of SME.

Title of Speech: Kinetostatic Analysis, Optimization and Remote Manipulation of Parallel/Hybrid Mechanisms

Abstract: In this talk, several new types of spatial parallel kinematic mechanisms with prismatic/revolute actuators whose degree of freedom is dependent on a constraining passive leg connecting the base and the platform are introduced. A generic kinetostatic model is established with the consideration of the characteristics of joints and links flexibilities. The model is used to demonstrate that flexible links have significant effects on the stiffness and accuracy of parallel kinematic machines. Stiffness mappings are shown and design guidelines for parallel kinematic machines are concluded.
The optimization of system parameters in achieving a better system stiffness is performed. This includes the development of a more explicit representation of an objective function in the optimization model. The genetic algorithm is employed to solve this optimization problem. As a result, a significant improvement of the system stiffness is achieved.
Finally, the remote manipulation with Java 3D is implemented and a sample is demonstrated.

Prof. Makoto Iwasaki

Nagoya Institute of Technology, Japan

Biography: Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. Since 1991, he has been with the Department of Computer Science and Engineering, Nagoya Institute of Technology, where he is currently a Professor at the Department of Electrical and Mechanical Engineering. As professional contributions of the IEEE, he has been an AdCom member of IES in term of 2010 to 2019, a Technical Editor for IEEE/ASME TMech from 2010 to 2014, an Associate Editor for IEEE TIE since 2014, a Management Committee member of IEEE/ASME TMech (Secretary in 2016 and Treasurer in 2017), and a Co-Editors-in-Chief for IEEE TIE since 2016, respectively. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design". He has received the Best Paper Award of Trans of IEE Japan in 2013, the Best Paper Award of Fanuc FA Robot Foundation in 2011, the Technical Development Award of IEE Japan in 2017, and The 3rd Nagamori Awards in 2017, respectively. His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

Title of Speech: Fast and Precision Motion Control for Industrial Positioning Devices

Abstract: Fast-response and high-precision motion control is one of indispensable techniques in a wide variety of high performance mechatronic systems including micro and/or nano scale motion, such as data storage devices, machine tools, manufacturing tools for electronics components, and industrial robots, from the standpoints of high productivity, high quality of products, and total cost reduction. In those applications, the required specifications in the motion performance, e.g. response/settling time, trajectory/settling accuracy, etc., should be sufficiently achieved. In addition, the robustness against disturbances and/or uncertainties, the mechanical vibration suppression, and the adaptation capability against variations in mechanisms should be essential properties to be provided in the performance.
The keynote speech presents the fast and precision motion control techniques, where a 2-degrees-of-freedom (2-DoF) control framework is especially handled as one of practical and/or promising approaches to improve the motion performance. Actual issues and relevant solutions for each component in the 2-DoF control structure are clarified, and then, one of examples, a 2-DoF controller design for robust vibration suppression positioning, is presented as an application to industrial high precision positioning devices.

Prof. WEIDONG Chen (陈卫东 教授)

Shanghai Jiao Tong University, China
（上海交通大学）

Biography: Weidong Chen received his B.S. and M.S. degrees in Control Engineering in 1990 and 1993, and Ph.D. degree in Mechatronics in 1996, respectively, all from the Harbin Institute of Technology, Harbin, China. Since 1996, he has been at the Shanghai Jiao Tong University where he is currently Chair and Professor of the Department of Automation, and Director of the Institute of Robotics and Intelligent Processing. He is the founder of the Autonomous Robot Laboratory. From August 2003 to February 2004, he was a visiting associate professor in the Department of Electrical and Computer Engineering at The Ohio State University. In July and August 2012, he was a visiting professor in the Artificial Intelligence Laboratory at the University of Zurich in Switzerland. He is a visiting professor in the Brain Science Life Support Research Center at the University of Electro-Communications in Japan from 2016 to 2018.
Dr. Chen’s current research interests include autonomous robotics, collective robotics, assistive robotics, and medical robotics. His research has been supported by National Natural Science Foundation of China (NSFC), National High-tech R&D Program (863 Program), Ministry of Education (MOE), etc. Dr. Chen has served on numerous program and organizing committees of international conferences. Most recently, he was an Organizing co-Chairs of the 2015 International Symposium on InfoComm & Media Technology in Bio-Medical & Healthcare Application (2015 IS-3T-in-3A), Chiba, Japan, November 15-18, 2015, and is serving as the General Chair of the 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, China, July 3-7, 2016.
Personal website: http://robotics.sjtu.edu.cn/

Title of Speech: Improving Autonomy and Safety of Intelligent Wheelchair

Abstract:
Intelligent wheelchair is a typical mobility assistive robot designed to assist a user with a physical disability or cognitive impairments. Intelligent wheelchairs are interacting closely with humans and performing navigation tasks in human environments with unpredictable changes, the autonomy and safety issues are more essential for these complex and challenging situations. In this talk, I will introduce our recent work on intelligent wheelchair with emphasis on autonomy and human-safety in the aspects of mapping, localization, navigation and human-robot interaction. The prototype systems of intelligent wheelchair developed in our lab, as well as experimental studies in real and dynamic environments will be presented for illustrating our methodologies and applications.

Prof. Juntao Fei (费俊涛教授)

Hohai University, China (中国河海大学)

Biography: Professor Juntao Fei received his B.S. degree from the Hefei University of Technology in 1991, M.S. degree from University of Science and Technology of China in 1998, M.S and Ph.D. degree from the University of Akron, USA in 2003 and 2007 respectively. He was a visiting scholar at University of Virginia, USA from 2002 to 2003, North Carolina State University, USA from 2003 to 2004 respectively. He served as an assistant professor at the University of Louisiana, USA from 2007 to 2009. Since May 2009, He has been a Professor at the College of IoT Engineering, Hohai University , Director of Institute of Electrical and Control Engineering. His research interests include adaptive control, intelligent control, sliding mode control, power electronics and control, mechatronics and robotics, smart material and structure. He is a Senior Member of IEEE. He has served as an associate editor for Transactions of the Institute of Measurement and Control, reviewers for numerous international journals, program committee members and chairs for numerous international conferences. He has published more than 200 journal and conference papers and 5 books and led more than 20 funded research projects to completion as Principal Investigator. He authorized 40 invention patents. He is an awardee of the Recruitment Program of Global Experts (China). His biography has been included in Who’s Who in the World, Who’s Who in Science and Engineering, Who’s Who in America.

Title of Speech: Adaptive Sliding Mode Control Using Double Loop Recurrent Neural Network for Dynamic Systems

Abstract: In this study, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer recurrent neural network(RNN) is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback neural network(NN) and external feedback neural network, the new structure can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular neural networks without feedback loops or the regular recurrent neural networks with a single feedback loop. The new proposed DLRNN structure is employed in the equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are online updated by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the adaptive neural sliding controller with DLRNN. Simulation results demonstrate that the proposed control system can achieve good tracking performance and the comparisons of approximation performance between Radial Basis Function(RBF) NN, RNN and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with fast speed while the internal states of DLRNN are more stable.

Prof. Ghassan Beydoun

University of Technology Sydney, Australia

Biography: Professor Ghassan Beydoun is currently based at the Faculty of Engineering and Information Technology in University of Technology Sydney, where he is also deputy Head of School (Research) Systems, Management and Leadership at the University of Technology Sydney. He is also an adjunct senior research fellow at the School of Information Systems, Management and Technology at the University of New South Wales, an associate editor of the International Journal of Intelligent Information Technologies (IJIIT) and an Editorial member of the Journal of Software. He received a degree in computer science and a PhD degree in knowledge systems from the University of New South Wales in 2000. His research interests include multi agent systems applications, ontologies and their applications, and knowledge acquisition. He is currently working on a project sponsored by an Australian Research Council Discovery Grant to investigate the best uses of ontologies in developing methodologies for complex systems and another project with SES on exploring the use of ontologies for flood management decision support. He has authored more than 100 journal and conference papers in these areas over the past 15 years. His most recent publication appeared in IEEE Transactions of Software Engineering, Information Systems journal, Information and Management, International Journal of Human Computer Studies, Information Processing and management and others.

Title of Speech: Agent Based Models as a Disaster Management Decision Tool

Abstract: Over the past nine years, we have undertaken research to simplify decision making processes for Disaster Management processes. In this talk, I will describe our approach which faciltates context identification and complexity management of Disaster Management scenarios. An agent based modelling process in combination a metamodelling is used to support decision makers to develop a variety of domain solutions models based on mixing and matching solutions from prior experiences. In developed countries, for recurring disasters (e.g. floods), there are dedicated document repositories of Disaster Management Plans (DMP) that can be accessed as needs arise. I will describe an agent-based knowledge analysis method to convert DMPs into a collection of knowledge units that can be stored into a unified repository based. We use the flood management plans used by New South Wales State Emergency Services in Australia to illustrate the approach. I will also conclude by examining the challenges on the horizon of integrating real time constraints in the DSS output.